The Fault Diagnosis of Rolling Bearing based on MED and HHT

DOI

10.2991/lemcs-15.2015.53How to use a DOI?

Keywords

Rolling bearing; Optimal filter; Minimum entropy deconvolution; HHT; Fault Diagnosis

Abstract

As an essential part of rotating machinery, the early fault diagnosis of rolling bearing can improve the safety of mechanical operation. What’s more, the machining accuracy can be effectively ensured. Unfortunately, the early fault signal of rolling bearing is extremely weak, which can be easily covered by others. And the endpoint effect will appear in several Intrinsic Mode functions (IMFs) when it is decomposed by Hilbert-Huang transform (HHT), which makes it be difficult to find the fault position accurately. To get the real components, the minimum Entropy Deconvolution (MED) method is proposed here to obtain the effective impact components with an inverse filter, which can improve the signal-to-noise ratio. The MED method which can effectively extract the useful information of the rolling bearing’s fault signal, and fully suppress the endpoint effect of Empirical Mode decomposition (EMD), as well as greatly improve the ability to precisely find the fault position, which has been confirmed by a lot of experiments, which has a great practical value in the practical production.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhidong Wang
AU  - Rui Huo
AU  - Daokun Zhang
PY  - 2015/07
DA  - 2015/07
TI  - The Fault Diagnosis of Rolling Bearing based on MED and HHT
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 278
EP  - 282
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.53
DO  - 10.2991/lemcs-15.2015.53
ID  - Wang2015/07
ER  -